Cocoa (Theobroma cacao) is the main raw material for the production of chocolate; it is considered the food of the gods, as it possesses a diversity of bioactive compounds beneficial to human health. The abundance of bioactive compounds, among others, is conditioned by the post-harvest processing of cocoa beans, and fermentation is a major step in this regard. Consequently, this research evaluated the changes in phenolic compounds and methylxanthines occurred in the fermentation of Criollo and CCN-51 cocoa beans, varieties of great commercial interest for the cocoa-growing areas of Peru. For this purpose, samples were taken every 12 h of cocoa beans under fermentation for 204 h in which phenols (gallic acid, caffeic acid, catechin, and epicatechin) and methylxanthines (theobromine, caffeine and theophylline) were quantified by ultra-high performance liquid chromatography (UHPLC); total polyphenols by Folin Ciocalteu; antioxidant capacity by DPPH free radical capture method; total anthocyanins; pH; titratable acidity; and fermentation rate of beans. We found that during fermentation, phenolic content, antioxidant activity, and methylxanthines of cocoa beans decreased; on the other hand, the anthocyanin content increased slightly. Indeed, at distinctly degree, fermentation influences bioactive compounds in cocoa beans, depending on the variety cultivated.

Postharvest handling and storage of yellow pitahaya ( Hylocereus megalanthus ) posed challenges due to the variability in physicochemical and bioactive properties at different maturity ( M ) stages and over time. This study is aimed at assessing the influence of the M and storage time ( T ) on the physicochemical and bioactive characteristics of yellow pitahaya. Fruits at three M stages (green, semiripe, and ripe) were collected, stored at 16°C with 85% relative humidity, and evaluated over 20 days (Days 1, 5, 10, 15, and 20). The research analyzed how M and T affected key quality attributes and bioactive components, identifying the M stage that optimized these properties for consumption. Physicochemical changes, as well as chemical composition and antioxidant capacity of 1‐day fruit powders, were assessed. Fruit dimensions, moisture, and firmness were mainly influenced by M , while weight loss, pH, soluble solids, and acidity were influenced by T . Color showed significant sensitivity to both factors. Green fruits were the smallest, with the highest firmness and lowest volume, while ripe fruits had the lowest epicarp percentage and thickness. During storage, significant reductions in dimension, volume, epicarp thickness, soluble solids, and acidity were observed, while weight loss, mesocarp + endocarp, color, pH, and M index increased. Chemical composition and bioactive properties varied notably with M stage, with semiripe and ripe fruits exhibiting the most favorable bioactive characteristics. In conclusion, both M and T significantly influenced most physicochemical and bioactive properties of yellow pitahaya, highlighting the semiripe and ripe stages as optimal for harvest, offering practical insights for improving postharvest handling and storage practices.

Livestock farming is vital to a country's economy, and technological innovations in animal genetics and reproductive biotechnologies are key for environmental and socioeconomic development. This study aimed to?evaluate the antioxidant capacity...

This study evaluated the integration of non-invasive remote sensing and colorimetry to classify the maturity stages of Coffea arabica fruits across four varieties: Caturra Amarillo, Excelencia, Milenio, and Típica. Multispectral signatures were captured using a Parrot Sequoia camera at wavelengths of 550 nm, 660 nm, 735 nm, and 790 nm, while colorimetric parameters L*, a*, and b* were measured with a high-precision colorimeter. We conducted multivariate analyses, including Principal Component Analysis (PCA) and multiple linear regression (MLR), to identify color patterns and develop predictors for fruit maturity. Spectral curve analysis revealed consistent changes related to ripening: a decrease in reflectance in the green band (550 nm), a progressive increase in the red band (660 nm), and relative stability in the RedEdge and near-infrared regions (735–790 nm). Colorimetric analysis confirmed systematic trends, indicating that the a* component (green to red) was the most reliable indicator of ripeness. Additionally, L* (lightness) decreased with maturity, and the b* component (yellowness to blue) showed varying importance depending on the variety. PCA accounted for over 98% of the variability across all varieties, demonstrating that these three parameters effectively characterize maturity. MLR models exhibited strong predictive performance, with adjusted R² values ranging between 0.789 and 0.877. Excelencia achieved the highest predictive accuracy, while Milenio demonstrated the lowest, highlighting varietal differences in pigmentation dynamics. These findings show that combining multispectral imaging, colorimetry, and statistical modeling offers a non-destructive, accessible, and cost-effective method for objectively classifying coffee maturity. Integrating this approach into computer vision or remote sensing systems could enhance harvest planning, reduce variability in specialty coffee lots, and improve competitiveness by ensuring greater consistency in cup quality.